Histogram of the node strength and histogram of the edge weight: two new features for RGB-D person re-identification

Abstract

Person re-identification is a classical task for any multi-camera surveillance system. Most of the existing researches on re-identification are based on features extracted from RGB images. However, there are many deficiencies in RGB image processing, some of which are requiring a lot of illumination and high computation. In this paper, novel features are proposed for RGB-D person re-identification. First, the complex network approach in texture recognition is modified and its threshold function is changed for using in depth images extracted by RGB-D sensors. Then, two novel measurements named the histogram of the edge weight (HEW) and the histogram of the node strength (HNS) are introduced on complex networks. Our features fit both single-shot and multi-shot person re-identification. In the single-shot case, the HNS is extracted from only one frame while for the multi-shot case it is extracted from both one frame and multi-frames. These proposed measurements are called histogram of the spatial node strength (HSNS) and histogram of the temporal node strength (HTNS) respectively. Subsequently, these measurements are combined with skeleton features using score-level fusion. The method is evaluated using two benchmark databases and the results show that ours outperforms some state-of-the-art methods.